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The primary objective of this study was to detect vegetation disturbance resulting from visitor use by using remote sensing. A pre-classification change detection analysis based on the normalized difference vegetation index was utilized to measure the amount of vegetation cover changes at Cadillac Mountain summit, Acadia National Park, Maine. By analyzing new remote sensing data collected in 2010 and 2018, we compared the vegetation conditions at the summit (experimental site) with a nearby site with little or no visitor use (control site). Additionally, the study was designed to examine vegetation cover changes between 2001–2007 (the first time frame) and 2010–2018 (the second time frame). Similar to the results observed in the first time frame, the experimental and control sites exhibited more vegetation increase than vegetation decrease in the second time frame. The amount of vegetation increase was 1,425m2 at the experimental site and 400m2 at the control site. The amount of vegetation decrease was 150m2 at the experimental site and 75m2 at the control site. Measurable vegetation cover changes based on the remote sensing analysis could provide baseline data for monitoring further changes over an extended period of time. The advantages of using remote sensing in detecting vegetation conditions are also discussed, along with management and research implications. Keywords: remote sensing, recreation ecology, vegetation, visitor impact monitoring, parks and protected areas

Providing quality hands-on exercises in a GIS course is essential in enhancing problem-solving skills and student-led learning activities. However, the strategies and instructional methods of managing hands-on exercises, which have been used in traditional face-to-face classes, will require new interpretations in a virtual learning environment and may need modifications to maximize the advantages of virtual technology and its use. Thus, in this brief review, I will mainly address what I learned while teaching a virtual GIS lab course. I will also make recommendations associated with implementing hands-on exercises in a virtual environment.

Abstract Background Oral delivery is a highly desirable property for candidate drugs under development. Computational modeling could provide a quick and inexpensive way to assess the intestinal permeability of a molecule. Although there have been several studies aimed at predicting the intestinal absorption of chemical compounds, there have been no attempts to predict intestinal permeability on the basis of peptide sequence information. To develop models for predicting the intestinal permeability of peptides, we adopted an artificial neural network as a machine-learning algorithm. The positive control data consisted of intestinal barrier-permeable peptides obtained by the peroral phage display technique, and the negative control data were prepared from random sequences. Results The capacity of our models to make appropriate predictions was validated by statistical indicators including sensitivity, specificity, enrichment curve, and the area under the receiver operating characteristic (ROC) curve (the ROC score). The training and test set statistics indicated that our models were of strikingly good quality and could discriminate between permeable and random sequences with a high level of confidence. Conclusion We developed artificial neural network models to predict the intestinal permeabilities of oligopeptides on the basis of peptide sequence information. Both binary and VHSE (principal components score Vectors of Hydrophobic, Steric and Electronic properties) descriptors produced statistically significant training models; the models with simple neural network architectures showed slightly greater predictive power than those with complex ones. We anticipate that our models will be applicable to the selection of intestinal barrier-permeable peptides for generating peptide drugs or peptidomimetics.

N-nitrosamines and their precursors found in cosmetics may be carcinogenic in humans. Thus the aim of this study was to carry out risk assessment for N-nitrosamines (N-nitrosodiethanolamine [NDELA], N-nitrosodiethylamine [NDEA]) and amines (triethanolamine [TEA], diethanolamine [DEA]) levels in cosmetics determined using validated liquid chromatography-tandem mass spectrometry (LC-MS/MS) procedures. NDELA and NDEA concentrations were present at levels of “not detected” (N.D.) to 596.5 μg/kg and N.D. to 40.9 μg/kg, respectively. TEA and DEA concentrations ranged from N.D. to 860 μg/kg and N.D. to 26.22 μg/kg, respectively. The nitrite concentration (3-2250 mg/l), number of nitrosating agents to a maximum 5, and pH (3.93-10.09) were also assessed. The impact of N-nitrosamine formation on the levels of TEA, DEA, nitrite, and other nitrosating agents was also examined. N-nitrosamine concentrations correlated with the number of nitrosating agents and nitrite concentrations. Data demonstrated that higher nitrite concentrations and a greater number of nitrosating agents increased NDELA and NDEA yields. Further, the presence of TEA and DEA exerted a significant influence on N-nitrosamine formation. Risk assessments, including the margin of exposure (MOE) and lifetime cancer risk (LCR) for N-nitrosamines and margin of safety (MOS) for amines, were calculated using product type, use pattern, and concentrations. Exposure to maximum amounts of NDELA and NDEA resulted in MOE > 10,000 (based upon the benchmark dose lower confidence limit 10%) and LCR <1 × 10−5, respectively. In addition, TEA and DEA concentrations in cosmetic samples resulted in MOS values >100. Therefore, no apparent safety concerns were associated with cosmetic products containing NDELA, NDEA, TEA, and DEA in this study. However, since amines and nitrosating agents produce carcinogenic nitrosamines, their use in cosmetics needs to be minimized to levels as low as technically feasible. [ABSTRACT FROM AUTHOR]